摘要
针对脑机接口(BCI)系统中常用的运动想象脑电信号,提出多特征融合的方法,提高其分类准确率。采用自回归(AR)模型、小波包变换、共空间模式(CSP)对BCI2003竞赛数据进行信号处理,提取AR模型系数、小波包熵差、方差作为特征,并在分类器上测试准确率。实验结果表明:多特征融合的分类准确率均高于单特征,保持在90%左右,可以更好地表征运动想象脑电信号。数据处理所选取的时间段在4-7s,实验结果具有较好的鲁棒性和泛化能力。
Proposes a method of multi-features fusion to improve the classification accuracy of motor imagery electroencephalogram signals commonly used in brain computer interface(BCI) system. Uses the autoregressive(AR) model, wavelet packet transform and common spatial pattern(CSP) to process BCI2003 competition data. AR model coefficient, extracts the wavelet package entropy difference and variance as the characteristic, and tests the accuracy on the classifier. The experimental results show that the classification accuracy of the multi-features fusion is higher than that of the single feature. It is maintained at about 90%, which can represent the motor imagery electroencephalogram signals better than the single feature. The time period of data processing is 4-7 s, and the experimental results have good robustness and generalization ability.
作者
陆振宇
陆旭峰
杨瑞洪
常珊
LU Zhen-yu;LU Xu-feng;YANG Rui-hong;CHANG Shan(Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University ofTechnology, Changzhou 213001;Yangzhou Polytechnic Institute, Yangzhou 225127)
出处
《现代计算机》
2019年第3期3-7,共5页
Modern Computer
基金
NSFC-广东联合基金(第二期)超级计算科学应用研究专项
国家自然科学基金(No.11647146
81603152)
江苏省六大人才高峰资助项目(No.2016-XYDXXJS-020)
江苏省产学研前瞻资助项目(BY2016030-06)
江苏省研究生科研与实践创新计划资助项目(No.SJCX17_0747)
江苏省研究生科研与实践创新计划资助项目(No.SJCX17_0748)
江苏省"青蓝工程"优秀青年骨干教师项目(苏教办师[2017]5号)
关键词
运动想象
AR模型
小波包变换
共空间模式
Motor Imagery
AR Model
Wavelet Packet Transform
Common Spatial Patterns